Atmospheric Modeling, Data Assimilation, and Predictability

frailty provides a more flexible choice compared with the widely used gamma or stable distributions. The framework adopted is fully Bayesian, and details are provided for the posterior updating steps via MCMC. Finally Chapter 20 deals with the well-studied linear mixed-effects regression formulation, where the random effects are modeled using a class of GSE distributions introduced earlier in Chapter 5. The likelihood inference is implemented via an EM algorithm. Simulation results are presented to assess the performance of the proposed models and methods, followed by an application to a well-known longitudinal study of cholesterol levels. Overall, this is an outstanding monograph on current research on skewelliptical models and its generalizations. Contributed by several experts in this area, the book does an excellent job presenting the depth of methodological research as well as the breath of application regimes. It will clearly be an extremely useful resource for researchers in pursuit of modeling skewness.

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